Paweł Liskowski
About Paweł Liskowski
Paweł Liskowski is a Research Scientist and Assistant Professor at Poznan University of Technology, specializing in deep learning and computational intelligence. He has a strong academic background in Computer Science, holding a PhD, MSc, and BSc from the same institution, and has contributed to various research papers in optimization and machine learning.
Current Position at NNAISENSE
Paweł Liskowski serves as a Research Scientist at NNAISENSE, a role he has held since 2019. He is based in Ticino, Switzerland, where he focuses on applying deep learning techniques to image classification and control problems. His work involves developing advanced algorithms and methodologies that contribute to the field of machine learning.
Academic Background
Paweł Liskowski has an extensive academic background in Computer Science from Poznan University of Technology. He earned his Bachelor of Science (BSc) from 2007 to 2011, followed by a Master of Science (MSc) from 2011 to 2012. He continued his studies at the same institution, achieving a Doctor of Philosophy (PhD) from 2012 to 2016.
Previous Experience at Poznan University of Technology
Before his current role, Paweł Liskowski worked at Poznan University of Technology as a Research Assistant from 2012 to 2018. He contributed to various research projects during this time. Additionally, he worked as a Software Engineer at NaviExpert from 2010 to 2013, gaining practical experience in software development.
Research Contributions and Publications
Paweł Liskowski has made significant contributions to the field of computational intelligence, particularly in evolutionary algorithms. He has co-authored several research papers, including works on ClipUp, a simple optimizer for policy evolution, Neuromemetic Evolutionary Optimization, and Program Synthesis as Latent Continuous Optimization. His research also includes projects involving neural network controllers and optimization algorithms.
Focus on Machine Learning Challenges
Liskowski actively participates in industry-driven challenges related to machine learning. His involvement in these challenges reflects his commitment to applying theoretical knowledge to practical problems, particularly in the areas of deep learning and optimization.